CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Short Term Electric Load Forecasting based on Artificial Neural Networks for Weekends of Baghdad Power Grid

by Ibraheem K. Ibraheem, Mohammed Omar Ali
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 3
Year of Publication: 2014
Authors: Ibraheem K. Ibraheem, Mohammed Omar Ali
10.5120/15484-4263

Ibraheem K. Ibraheem, Mohammed Omar Ali . Short Term Electric Load Forecasting based on Artificial Neural Networks for Weekends of Baghdad Power Grid. International Journal of Computer Applications. 89, 3 ( March 2014), 30-37. DOI=10.5120/15484-4263

@article{ 10.5120/15484-4263,
author = { Ibraheem K. Ibraheem, Mohammed Omar Ali },
title = { Short Term Electric Load Forecasting based on Artificial Neural Networks for Weekends of Baghdad Power Grid },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 3 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number3/15484-4263/ },
doi = { 10.5120/15484-4263 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:09:50.735427+05:30
%A Ibraheem K. Ibraheem
%A Mohammed Omar Ali
%T Short Term Electric Load Forecasting based on Artificial Neural Networks for Weekends of Baghdad Power Grid
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 3
%P 30-37
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This work presents proposed methodsfor short term power load forecasting (STPLF) for the governorate of Baghdad using two different models of Artificial Neural Networks (ANNs). The two models used in this work are the multi-layer perceptron (MLP) model trained with Levenberg-Marquardt Back Propagation (BP) algorithm and Radial Basis Function (RBF) neural network. Inputs to the ANN are thepast loadsvalues and the output of the ANN is the load forecast for the weekends of certain months for Baghdad governorate. The data is divided into two parts where half of them was used for training and the other half was used for testing the ANN. Simulations were achieved by MATLAB software with the aid of Neural networks toolbox, where the data obtained for the Iraqi national grid were rearranged and preprocessed. Finally, the simulations results showed that the forecasted load values for the Baghdad governorate by the proposed methods were very close to actual ones as compared with the traditional methods.

References
  1. K. Y. Lee, Y. T. Cha, and J. H. Park. 1992. Short-Term Load Forecasting Using An Artificial Neural Network. IEEE Transactions on Power Systems, Vol. 7, No. 1, pp. 124-132.
  2. J. H. Chow. 2005. Applied Mathematics for Restructured Electric Power Systems:Optimization, Control, and Computational Intelligence. New York: Springer Verlag, Ch. 12.
  3. G. Gross and F. D. Galiana. 1987. Short-term load forecasting. Proceedings of the IEEE, Vol. 75, No. 12, pp. 1558 – 1573.
  4. M. T. Haque and A. M. Kashtiban. 2005. Application of Neural Networks in Power System. A Review, Transactions on Engineering, Computing and Technology Vol. 6, June.
  5. S. S. Sharif and J. H. Taylor. 2000. Short term Load Forecasting by Feed Forward Neural Networks. Proc. IEEE ASME First Internal Energy Conference (IEC), Al Ain, United Arab Emirate.
  6. H. Chen, C. A. Canizares, and A. Singh. 2001. ANN-based Short-term load forecasting in Electricity Markets. Proc. IEEE on Power Engineering Society Winter Meeting, Vol. 2, pp. 411-415.
  7. G. A. Adepoju, S. O. A Ogunjuyigbe, and K. O Alawode. 2007. Application of Neural Network to Load Forecasting in Nigerian Electrical Power System. The Pacific Journal of Science and Technology, Vol. 8, No. 1, May.
  8. S. N. , A. Yelamali, and K. Byahatti. 2010. Electricity Short term Load Forecasting using Elman Recurrent Neural Network. International Conference on Advances in Recent Technologies in Communication and Computing.
  9. S. K. Sheikh and M. G. Unde. 2012. Short Term Load Forecasting Using ANN Technique. International Journal of Engineering Science and Emerging Technologies, Vol. 1, No. 2, pp. 97-107.
  10. N. A. Salim, T. K. Abdul Rahman, M. F. Jamaludin, and M. F. Musa. 2009. Case Study of Short Term Load Forecasting for Weekends. Proc. IEEE Student Conference on Research and Development, Malaysia.
  11. J. P. Rothe, A. K. Wadhwani, and Mrs. S. Wadhwani. 2009. Short Term Load Forecasting Using Multi Parameter Regression. International Journal of Computer Science and Information Security(IJCSIS), Vol. 6, No. 2.
  12. Azzam-ul-Asar, Syed RiazulHassnain, Affan Khan. 2007. Short Term Load Forecasting Using Particle Swarm Optimization Based ANN Approach,Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12-17.
  13. M. A. Farahat, M. Talaat. 2012. Short-Term Load Forecasting Using Curve Fitting Prediction Optimized by Genetic Algorithms", International Journal of Energy Engineering, 2(2): 23-28.
  14. X. Cui, T. E. Potok and P. Palothingal. 2005. Document Clustering using Particle Swarm Optimization, IEEE, May.
  15. S. Haykin. 1999. Neural Networks", 2nd ed. , A Comprehensive Foundation, MacMillan Publishing, Englewood Cliffs, N. J.
Index Terms

Computer Science
Information Sciences

Keywords

Load forecasting multilayer perceptron radial basis neural networks (RBF) Back Propagation.

Powered by PhDFocusTM